Ryan McKenna has recently won the National Institute of Standards and Technology’s (NIST) Differential Privacy Synthetic Data Challenge Match #3. The competition tests participants’ ability to identify and develop practical methods for creating differentially private synthetic data sets. The challenges include empirical evaluation of synthetic data generation on common tasks […]

“Spade: A Modular Framework for Analytical Exploration of RDF Graphs” Yanlei Diao, Paweł Guzewicz, Ioana Manolescu, Mirjana Mazuran “UDAO: A Next-Generation Unified Data Analytics Optimizer” Khaled Zaouk, Fei Song, Chenghao Lyu, Arnab Sinha, Yanlei Diao, Prashant Shenoy “PSynDB: Accurate and Accessible Private Data Generation” Zhiqi Huang, Ryan McKenna, George Bissias, […]

Marco Serafini’s paper “Choosing A Cloud DBMS: Architectures and Tradeoffs” was accepted to VLDB 2019. The paper was featured by the morning paper, a popular research blog. Authors: Junjay Tan, Matthew Perron, Xiangyao Yu, Thanaa Ghanem, Michael Stonebraker, David DeWitt, Marco Serafini, Ashraf Aboulnaga, Tim Kraska

Ryan McKenna has recently won fourth place and a $3,000 prize in the National Institute of Standards and Technology’s (NIST) second match in the Differential Privacy Synthetic Data Challenge. The competition tests participants’ ability to identify and develop practical methods for creating differentially private synthetic data sets. The challenges include […]